{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# PaddlePaddle-Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Matplotlib 由各种可视化类组成，内部结构复杂\n",
    "\n",
    "matplotlib.pyplot是绘制各类可视化图形的命令字库"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#显示matplotlib生成的图形\n",
    "%matplotlib inline\n",
    "\n",
    "x=np.linspace(-1,1,50)  #构造等差数列\n",
    "y=2*x+1\n",
    "\n",
    "#传入x,y，通过plot()绘制出折线图\n",
    "plt.plot(x,y)\n",
    "\n",
    "#显示图形\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 504x360 with 1 Axes>",
      "image/png": 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3dhQ6HUVE/NybOwoprazx64uAW1NZuciUIX0YEh/Nnz86gLW6SFhE3MNay58/OsDQvtFMHtzH6Tgeo7JyEWMM35oygM8KT/BxdpnTcUTET32UXcrewhMs9POLgFtTWbnQ9WP7ER8TzrPr9jsdRUT81LPr9tO3ezjXj+3ndBSPUlm5UHhIMLdPHsBH2aXsPlzudBwR8TO78sv5OLuM2y8ZQHhIsNNxPEpl5WLzJqQQEx6ioysRcbln1+8nJjyEuRP8/yLg1jpVVsaYq40xmcaYbGPMA2fYZpoxZrsxZo8xZp1rY/qO7hGhzJuYwspdhRwqO+V0HBHxE7llJ1m1q5CvT+xP94hQp+N4XIdlZYwJBp4CrgFGAHONMSNabdMDeBr4qrV2JDDH9VF9x+2XDCAkKIg/fpjjdBQR8RN//DCHkKAgbr8k1ekojujMkdV4INtam2OtrQVeAWa12mYesMxaewjAWlvs2pi+pW/3CG4Y24+lGXmUagomETlHpZU1/CMjn9lp/YjvHuF0HEd0pqz6AXktvs9vvq2loUBPY8xaY8wWY8x8VwX0VXdcOpDahkZe3HDQ6Sgi4uP+uuEgtQ2N3HHpQKejOKYzZdXWQP7WV72GAOOAa4GrgIeMMUO/9EDG3GmMyTDGZJSU+PeChYPjo7lieF/+ujGXkzX1TscRER91sqaeFzfmcuWIvgyKC4ypldrSmbLKB5JbfJ8EFLSxzWpr7UlrbSmwHhjT+oGstc9Za9OttelxcXFnm9ln3DV1EOVVdfx9c17HG4uItOGVzXmUV9Vx19RBTkdxVGfKajMwxBgzwBgTBtwCrGi1zRvAFGNMiDEmCpgA7HVtVN8zrn9Pxqf24s8fHaCuodHpOCLiY+oaGvnzhzmMH9CLtJSeTsdxVIdlZa2tB+4D3qapgJZaa/cYYxYZYxY1b7MXWA3sBD4F/mSt3e2+2L5j0bSBHD5exVs7Wx+Mioi0780dBRSUV3N3gB9VARinJl1NT0+3GRkZjuzbkxobLVc/vp4gY1j1nSkBNZeXiJw9ay1X/+5DAFZ/N3DeO4wxW6y16a1v1wwWbhYUZLjr0kF8XlTB2kz/HlQiIq7zQWYxmUcquGvqwIApqvaorDzgujGJJMRGaAomEem0Z9flkBgbwXVjEp2O4hVUVh4QFhLEwskD+OTAUbbkHnM6joh4uS25x/j0wFFunzyA0GC9TYPKymPmjk+hZ1QoT76/z+koIuLlnnh/H726hTEvACesPROVlYd0Cw/hW1MG8kFmCTvzjzsdR0S81I6846zNLGHh5AFEhYU4HcdrqKw8aP6k/nSPCOGJ97OdjiIiXuqJ97OJjQxl/qT+TkfxKiorD4qJCGXBJQN497Mj7C084XQcEfEynxWcYM3eIyy4JJWYAFwGpD0qKw+7/ZIBRIeH8KSOrkSklSc/2Ed0eAgLLh7gdBSvo7LysNioUL55cX9W7i4ku7jC6Tgi4iX2Halg1e4ibrs4ldgoHVW1prJywMLJA4kMDdbRlYh84ckPsokMDeb2yTqqaovKygG9uoXxjYn9WbGjgAOlJ52OIyIOO1B6kjd3FHDrxP706hbmdByvpLJyyLemNF3s9/QHOroSCXRPfZBNWEgQ35oSuIsrdkRl5ZD4mAjmjk9h+bbD5B095XQcEXFI3tFTLN92mLnjU4iLCXc6jtdSWTlo0dRBBBnDM5ozUCRgPb12P8GmacJrOTOVlYPOi41gTnoS/8jIo+B4ldNxRMTDCo5X8eqWPG6+KInzYiOcjuPVVFYOu3vaIKyFP+joSiTgnF6J4e5pgx1O4v1UVg5L6hnFjWlJLNmcR/GJaqfjiIiHFJ+o5pXNedyYlkS/HpFOx/F6KisvcM/0QTQ0Wn12JRJAnl67n4ZGyz06quoUlZUX6N+7GzelJbF40yF9diUSAAqOV/G3Tw4xZ1wSKb2jnI7jE1RWXuL+ywZjsZqRXSQAPNG8rt39lw1xOInvUFl5iaSeUcwdn8I/MvLILdOsFiL+6mDpSZZm5DN3fLI+q+oClZUXuW/6YIKDDI+v0WrCIv7q8ff2ERpsuHe6PqvqCpWVF4nvHsE3L05l+fbD7DuiGdlF/M2+IxW8vv0w35yUSnx3XVfVFSorL7No6iCiQoP57Zosp6OIiIs99m4W3cJCWDRVs1V0lcrKy/TqFsbCyQNYuauI3YfLnY4jIi6y+3A5q3YXcfvkAfTUzOpdprLyQgunDCQ2MpTH3tXRlYi/ePSdTGIjQ/nWFK1XdTZUVl4oNjKUOy8dyPufF7Ml95jTcUTkHG3JPcoHmSXcNXUg3SO0CvDZUFl5qQWXpNInOoxH38l0OoqInKPfvJ1Fn+gwbrs41ekoPktl5aWiwkK4e9pgNuwvY0N2qdNxROQsbcguZWNOGfdMG0xUWIjTcXyWysqLfX1CCud1j+A372RirXU6joh0kbWWX7+TSUJsBPMmpDgdx6eprLxYRGgw9182mK2HjrM2s8TpOCLSRR9kFrPt0HHu/8oQIkKDnY7j01RWXm7OuGRSekXxyNuZNDTq6ErEVzQ0Wh5ZnUlKryjmpCc5Hcfnqay8XFhIEN+/ahh7C0+wfNthp+OISCct25rP50UV/OCqYYQG6632XOkZ9AEzRyUwJimWR9/JpLquwek4ItKBqtoGHn0nizFJscwcneB0HL+gsvIBQUGGB2cMp7C8mj9/dMDpOCLSgec/PkDRiWr+Y8ZwjDFOx/ELKisfMXFgby4fHs8za/dTVlnjdBwROYPSyhqeWbufy4f3ZcLA3k7H8RsqKx/ywDXnU1XXoAUaRbzYE+/to6qugQeuOd/pKH5FZeVDBsfH8LWLknl5Uy4HSrVAo4i3ySmpZPEnh7jlomQGx0c7HcevqKx8zHcvH0JYSBCPrP7c6Sgi0sojqzMJCwniu5cPdTqK31FZ+Zj4mAjuvHQgq3YXsSX3qNNxRKRZxsGjrN5TxF2XDiIuJtzpOH5HZeWD7pgykLiYcH7xz72ahknEC1hreXjlXuJjwrnjUi0B4g6dKitjzNXGmExjTLYx5oF2trvIGNNgjLnJdRGltW7hIXzviqFsPXSct/cUOR1HJOCt3l3E1kPH+d4VQzVZrZt0WFbGmGDgKeAaYAQw1xgz4gzb/Qp429Uh5cvmjEtiSHw0v1qdSV1Do9NxRAJWbX0jv1r9OUP7RjMnPdnpOH6rM0dW44Fsa22OtbYWeAWY1cZ29wOvAcUuzCdnEBIcxIMzzudA6Un+9skhp+OIBKy/fZLLwbJTPHjNcIKDdAGwu3SmrPoBeS2+z2++7QvGmH7ADcCzrosmHZk+LJ6JA3vxuzVZlJ+qczqOSMApP1XH4+/tY9LA3kwbFud0HL/WmbJq61eF1p/q/w74kbW23YnrjDF3GmMyjDEZJSVa8uJcGWN4aOYIyqvq+O2aLKfjiASc367JoryqjodmjtC0Sm7WmbLKB1qeiE0CClptkw68Yow5CNwEPG2Mub71A1lrn7PWpltr0+Pi9FuIK4xMjGXehBRe2pRLZlGF03FEAsbnRSd4aVMuX5/QnxGJ3Z2O4/c6U1abgSHGmAHGmDDgFmBFyw2stQOstanW2lTgVeAea+3rrg4rbfv3K4YRHR7Cf7+5R0PZRTzAWst/r/iMmIimkbnifh2WlbW2HriPplF+e4Gl1to9xphFxphF7g4oHevZLYzvXzmUDfvLWL1bQ9lF3G3V7iI25pTx71cOo2e3MKfjBATj1G/i6enpNiMjw5F9+6P6hkZmPvERFdX1rPneVCLDtIS2iDtU1TZw+WPr6B4Zylv3T9YIQBczxmyx1qa3vl0zWPiJkOAgfvrVkRw+XsWz6/Y7HUfEbz2zbj+Hj1fx0+tGqKg8SGXlRyYO7M11YxJ5dt1+8o6ecjqOiN/JO3qKZ9ft56tjErVWlYeprPzMg9ecT5AxPLxyr9NRRPzOL/65l2BjeHCG1qryNJWVn0nsEcm90wexancRH2eXOh1HxG98tK+U1XuKuO8rg0mIjXQ6TsBRWfmhb00ZSHKvSP77zT2aN1DEBeoaGvnvN/eQ0iuKhZM1q7oTVFZ+KCI0mIeuHUHWkUpe3pTrdBwRn/fSxlz2FVfy0MwRRIRqpK0TVFZ+6ooRfbl0aByPvZtFSUWN03FEfFZJRQ2/XZPF1KFxXD483uk4AUtl5aeMMfzXdSOoqWvk5//8zOk4Ij7rZ299Rk1dIz+5TvP/OUll5ccGxUVz97RBvLG9gHVZmjhYpKvWZhazYkcB90wfxKC4aKfjBDSVlZ+7Z/ogBvbpxo9f30VVbbuT4otIC1W1DTz0xm4GxnXj7mmDnI4T8FRWfi48JJhf3DCKvKNVPP7ePqfjiPiM372XRd7RKh6+YRThIRpU4TSVVQCYNKg3c8Yl8acPc/i86ITTcUS83t7CE/zpwwPcnJ7ERM1U4RVUVgHiP2YMp3tkKA8u20Vjo5YRETmThkbLg8t20SMylP+YMdzpONJMZRUgenYL46GZw9l26DiLP9G1VyJnsviTXLbnHeehmSPoEaXlP7yFyiqAXH9hPyYP7sMjqzM5cqLa6TgiXqeovJpHVmcyZUgfZl2Y6HQcaUFlFUCMMfz8+guobWjkpyv2OB1HxOv8dEXTFGU/v/4CXVPlZVRWASa1Tze+fdkQVu0uYs1nR5yOI+I13v3sCKv3FPHty4bQv3c3p+NIKyqrAHTHlIEM7RvNT97YzcmaeqfjiDiusqaen7yxm2F9Y7jz0oFOx5E2qKwCUFhIEP87exSFJ6r51erPnY4j4rhfrfqcohPVPDx7FKHBelv0RvpbCVDj+vfitotTeXFjrta9koD2cXYpL23KZcHFAxjXv6fTceQMVFYB7IdXnc+APt344as7qaiuczqOiMdVVNfxw1d3MrBPN3549TCn40g7VFYBLDIsmN/MGUNheRW/+Odep+OIeNzP39pLYXkVv7l5jNap8nIqqwA3rn9P7rx0EK9szuODz4udjiPiMe9/foS/Z+Rx19RBpKXo9J+3U1kJ/3bFEIb2jeaBZTspP6XTgeL/jp+q5YHXdjGsbwzfvXyI03GkE1RWQnhIMI/OuZCyylp++qYuFhb/99MVezh6spZHbx6jGdV9hMpKABiVFMu90wezfNthVu8ucjqOiNus3l3I69sLuO8rg7mgX6zTcaSTVFbyhfu+MpiRid35z+W7KKuscTqOiMuVVdbwn8t3c0G/7tw7fbDTcaQLVFbyhdDgIB69eQwV1fU89MZurNVSIuI/rLX8+PXdVFTX8+icC3Xxr4/R35b8i/PP6853rxjCyl1FrNhR4HQcEZdZsaOAVbuL+LcrhjLsvBin40gXqazkS+6cMpCxKT348eu7yTt6yuk4Iucs7+gpfrx8N2kpPTT3n49SWcmXhAQH8ftbxoKF+5dso66h0elIImetrqGR+5dsAwOP3zKW4CAt/eGLVFbSpuReUfzyxtFszzvOY+9mOR1H5Kw9+k4W2/OO88vZo0nuFeV0HDlLKis5o2tHJzB3fDLPrN3P+qwSp+OIdNn6rBKeXbefueNTuHZ0gtNx5ByorKRdP5k5kiHx0Xxv6Q5KKjScXXxHcUU131u6vWnttpkjnI4j50hlJe2KDAvmyXlpVFTX8b2l22ls1HB28X6NjZZ/X7qDypp6npyXRmSYZqnwdSor6dCw82L4r+tG8uG+Up77MMfpOCId+sP6HD7cV8p/XTeSoX01TN0fqKykU+aOT+baUQn85u1Mth465nQckTPaknuM37yTybWjErjlomSn44iLqKykU4wxPDx7FH27R/DtJdsor9Ls7OJ9yqvq+PaSbSTERvDw7FEYo2Hq/kJlJZ0WGxnKE/PGUlhezYPLdmo6JvEq1loeXLaTIyeq+f3cscRGhjodSVxIZSVdkpbSkx9cNYyVu4r4oz6/Ei/y3PocVu4q4gdXDdNiin6oU2VljLnaGJNpjMk2xjzQxv1fN8bsbP6zwRgzxvVRxVvcdelAZow6j1+u+pwP9+n6K3Heh/tK+NXqz7l2VIKmU/JTHZaVMSYYeAq4BhgBzDXGtL5o4QAw1Vo7GvgZ8Jyrg4r3MMbw65vGMCQ+hvuXbNP8geKoQ2WnuO9v2xjaN4ZHbhqtz6n8VGeOrMYD2dbaHGttLfAKMKvlBtbaDdba00PENgFJro0p3qZbeAjPzR9HY6PljhczOFVb73QkCUCnauu586UMAP5w6zi6hYc4nEjcpTNl1Q/Ia/F9fvNtZ7IQWHUuocQ39O/djd/PHUvmkQp+9NouDbgQj7LW8sNXd5J5pILfzx1L/97dnI4kbtSZsmrrmLrNdyVjzHSayupHZ7j/TmNMhjEmo6REn3X4g2nD4vn+lcN4c0eBBlyIRz23Poe3dhbyg6uGMXVonNNxxM06U1b5QMsr65KAL63KZ4wZDfwJmGWtLWvrgay1z1lr06216XFxenH5i3umDdKAC/GolgMq7p46yOk44gGdKavNwBBjzABjTBhwC7Ci5QbGmBRgGXCrtVbrSQQYDbgQT9KAisDUYVlZa+uB+4C3gb3AUmvtHmPMImPMoubNfgL0Bp42xmw3xmS4LbF4pdYDLk7WaMCFuN7JGg2oCFTGqQ/F09PTbUaGOs3frMsqYcELnzJ1aBx/nJ9OSLCuOxfXqG9o5I4XM1iXVcILC8brcyo/ZYzZYq1Nb3273knEpaYOjeNn11/AB5klPPTGbo0QFJew1vLQG7v5ILOEn18/SkUVgHQMLS739Qn9KThexVMf7Kdfj0ju+8oQpyOJj3vy/WyWfJrHfdMHM29CitNxxAEqK3GL7185jILj1fzmnSwSe0QyO03XicvZeW1LPo++m8Xssf349yuHOh1HHKKyErcwxvCrG0dz5EQ1P3x1J/ExEUwe0sfpWOJjPtxXwo9e28klg3vzyxs18i+Q6TMrcZuwkCCevXUcg+KiWfTyFvYWnnA6kviQzwpOcPfLWxkcH80z3xhHWIjergKZ/vbFrbpHhPLCgouIDg9hwQubKSyvcjqS+ICC41Us+MunRIeH8MKCi+geobWpAp3KStwusUckLyy4iJM19dz2/GatMiztKq+qY8ELmzlV08Bfbr+IhNhIpyOJF1BZiUcMT+jOs7eOI6e0kgUvfEqlLhqWNlTW1HPbC5+SU1rJs7eO4/zzujsdSbyEyko85pLBfXhi7lh25Jdz+wubtayI/ItTtfUseOFTduaX88TcNC4ZrAE58v9UVuJRV1+QwO++diEZuUdZ+JcMqmobnI4kXqCqtoGFf8lgS+4xHr/lQq6+4DynI4mXUVmJx103JpFHbx7DpgNl3PlSBtV1KqxAVl3XwJ0vZbDpQBmP3XwhM0cnOh1JvJDKShxxw9gkHrlxNB/uK+Xul7dQU6/CCkQ19Q0senkLH2WX8uubxnD92PbWdZVAprISx8xJT+bhG0bxQWYJ9y7eRm19o9ORxINq6xu5d/FW1maW8PANo7hpnGY5kTNTWYmj5k1I4X9mjWTN3iN855Vt1DeosAJBXUMj316yjTV7i/nZrJHMHa/5/qR9Kitx3PxJqfz42uGs2l3E/Uu26ZSgn6uua+DbS7axek8RD80cwa2TUp2OJD5AcwOKV/jWlIEYY/jZW59x/PnNPDd/HDGatcDvVFTXcceLGWzKOcpDM0ewcPIApyOJj9CRlXiNhZMH8NuvjWHzwaPc8twmSipqnI4kLlRcUc3X/rCJjIPH+O3XxqiopEtUVuJVbhibxB+/mU5OyUluenYDh8pOOR1JXCC37CQ3PbORA6Un+dM307lhrAZTSNeorMTrTB8Wz+I7JlBeVcfsZzawp6Dc6UhyDnYfLufGZzZSUV3H3+6YwLRh8U5HEh+kshKvlJbSk1cXTSIs2PC1P2xiw/5SpyPJWdiwv5RbnttEWLDhH4suZmxKT6cjiY9SWYnXGhwfw2v3XExCbAS3Pb+ZlbsKnY4kXbByVyG3Pb+ZxB4RvHbPxQyOj3Y6kvgwlZV4tYTYSP6xaBKjkmK5Z/FWfvtuFo2N1ulY0o7GRstj72Zxz+KtjEqKZeldk7TMh5wzlZV4vR5RYSz+1gRuTEvi8ff2cceLGZyo1ppY3qi8qmlo+u/f28dN45JY/K0J9IgKczqW+AGVlfiEiNBgfjNnNP8zayTrskqY9eTH7DtS4XQsaSHrSAXXP/Ux67JK+Nmskfz6ptFEhAY7HUv8hMpKfIYxhvmTUvnbHROpqK7j+qc+ZvVufY7lDVbtKuT6pz6morqeJXdO5NZJqRhjnI4lfkRlJT5n/IBevHX/FIb0jWHRy1t5ZPXnNOhzLEc0NFoeWf05dy/eytC+Mbx1/2QuSu3ldCzxQyor8UnnxUbw97smMnd8Mk+v3c+Cv2zWjBceVlJRw4K/bObptfuZOz6Fv981kfNiI5yOJX5KZSU+KzwkmP+dPZqHbxjFppwyrvrdelZpeLtHrNpVyFW/W8+mnDIevmEU/zt7FOEh+nxK3EdlJT5v3oQU/nn/ZPr1iOTuxVv57ivbKD+l0YLuUH6qju+8so27F28lqWckK789mXkTtLyHuJ9mXRe/MKRvDMvuuZinP9jPE+/vY2NOGb+6cbSm9nGhDzKLeeC1nZRV1vK9K4Zy97RBhAbr913xDL3SxG+EBgfxncuHsPyeS4iNDOW2Fzbz4LJdVNbUOx3Np1XW1PPgsp0seGEzsZGhvH7vJXz7siEqKvEoHVmJ3xmVFMuK+ybz2zVZPLc+h4+yS/ifWRcwXUdZXWKtZW1mCT9ZsZv8Y1XcNXUg37tiqD6bEkcYa50Z8puenm4zMjIc2bcEjoyDR/nhqzvJKT3JpUPj+PG1wxnaN8bpWF4v60gFP//nXtZnlTCwTzd+PWc04/prSLq4nzFmi7U2/Uu3q6zE39XWN/LSplweX5PFydoG5o1P4d+uGEqvbpoGqLWyyhp+uyaLJZ/m0S0smO9cPpRbJ/YnLESn/MQzVFYS8I6erOXxNVm8/MkhosKC+c5lQ5g/KVVvxDQV+l83HOT37+/jVG0D35iQwncvH0pPFbp4mMpKpNm+IxX8YuVe1maWkNo7iu9dOYwZF5xHSAAOGKhvaGTl7iIeeyeTg2WnmDas6VTp4HidKhVnqKxEWlmbWcwv/rmXfcWVJPWMZOHkAdycnky3cP8fd3Sypp6/b87jzx8d4PDxKobER/Of1w7XUH9xnMpKpA2NjZY1e4/w3PocMnKPERsZyjcmpvDNSanEd/e/qYOKT1Tzlw0HeXlTLieq67kotSd3TBnI5cP7EhSkiWfFeSorkQ5syT3GH9fn8PZnRYQGBXH92EQWXDKA4QndnY52zvYWnuCFjw/w+rYC6hobuXrkedxx6UDStMy8eBmVlUgnHSw9yZ8+yuEfGfnU1DcytG80M0cnMnN0AgPjfGdp9pySSt7aWchbOwvIOlJJRGgQc8Yls3DyAFL7dHM6nkibVFYiXXT0ZC1v7ijgrZ0FbD54DICRid2/KK7kXlEOJ/yyvKOnviioPQUnALgotSczRydy3ZhEDdcXr3dOZWWMuRp4HAgG/mSt/WWr+03z/TOAU8Bt1tqt7T2mykp8ScHxKlbuKuTNnYXsyDsOwOikWCYO7E1aSk/G9e9JXEy4x3OVVNSwJfcYWw8dY1NOGTvzywG4MLkHM0cncO3oBBJiIz2eS+RsnXVZGWOCgSzgCiAf2AzMtdZ+1mKbGcD9NJXVBOBxa+2E9h5XZSW+6vTRy5q9R9iVX05tQyMA/XtHMS6lJ2n9m8ortXc3IsNcNzVRVW0DB8tONpVT7jG2HDpGbtkpAMKCgxiVFMvlw/t67VGfSGecqaw6M0Z3PJBtrc1pfqBXgFnAZy22mQW8aJuab5MxpocxJsFaq8WFxO8k94ri7mmDuHvaIGrqG9h9+ERTeeQeY/2+UpZtO/zFtj2jQkmIjSSxRwQJsZEk9IggMTay3eHxJ2vqKSivovB4NYXlVRQ0//dYi2VP+kSHk96/J9+Y0J+0/j25oF93zdknfq0zZdUPyGvxfT5NR08dbdMPUFmJXwsPCWZc85HUHTRN/pp3tIptecfIP1ZFwfEqCsuryT9WxeaDxyiv6vw6W7GRoSTERpDYI5KxKT1I7BFJUs9I0lJ6ktQzkqaz7yKBoTNl1da/iNbnDjuzDcaYO4E7AVJStGCb+B9jDCm9o0jp3fZpuJM19RSWV1Nd13DGx4gIDSYhNiIgLk4W6azO/GvIB5JbfJ8EFJzFNlhrnwOeg6bPrLqUVMQPdAsPYXC87wx/F/EWnZkMbTMwxBgzwBgTBtwCrGi1zQpgvmkyESjX51UiIuIqHR5ZWWvrjTH3AW/TNHT9eWvtHmPMoub7nwVW0jQSMJumoesL3BdZREQCTadOiltrV9JUSC1ve7bF1xa417XRREREmgTemggiIuJzVFYiIuL1VFYiIuL1VFYiIuL1VFYiIuL1VFYiIuL1VFYiIuL1HFt80RhTAuS64KH6AKUueBxP8bW84HuZfS0v+F5m5XU/X8vsqrz9rbVxrW90rKxcxRiT0dbaJ97K1/KC72X2tbzge5mV1/18LbO78+o0oIiIeD2VlYiIeD1/KKvnnA7QRb6WF3wvs6/lBd/LrLzu52uZ3ZrX5z+zEhER/+cPR1YiIuLnfKKsjDFzjDF7jDGNxpgzjjYxxlxtjMk0xmQbYx5ocXsvY8y7xph9zf/t6ea8He7PGDPMGLO9xZ8TxpjvNt/3U2PM4Rb3zXA6b/N2B40xu5ozZXT15z2d2RiTbIz5wBizt/n1850W93nkOT7Ta7LF/cYY8/vm+3caY9I6+7MO5f16c86dxpgNxpgxLe5r8/XhBZmnGWPKW/xd/6SzP+tQ3h+0yLrbGNNgjOnVfJ/Hn2NjzPPGmGJjzO4z3O+Z17C11uv/AMOBYcBaIP0M2wQD+4GBQBiwAxjRfN8jwAPNXz8A/MrNebu0v+bsRTRdXwDwU+D7Hnx+O5UXOAj0Odf/X09lBhKAtOavY4CsFq8Jtz/H7b0mW2wzA1gFGGAi8Elnf9ahvBcDPZu/vuZ03vZeH16QeRrw1tn8rBN5W21/HfC+w8/xpUAasPsM93vkNewTR1bW2r3W2swONhsPZFtrc6y1tcArwKzm+2YBf23++q/A9W4J+v+6ur/LgP3WWldcJH02zvX58fTz26l9WmsLrbVbm7+uAPYC/TyQ7bT2XpOnzQJetE02AT2MMQmd/FmP57XWbrDWHmv+dhOQ5OZMHTmX58krn+NW5gJL3JypXdba9cDRdjbxyGvYJ8qqk/oBeS2+z+f/35j6WmsLoekNDIh3c5au7u8WvvyCvK/5kPp5D5xW62xeC7xjjNlijLnzLH7elbq0T2NMKjAW+KTFze5+jtt7TXa0TWd+1tW6us+FNP1GfdqZXh/u1NnMk4wxO4wxq4wxI7v4s67U6X0aY6KAq4HXWtzsxHPcEY+8hju1rL0nGGPWAOe1cdd/Wmvf6MxDtHGb24Y6tpe3i48TBnwVeLDFzc8AP6Mp/8+AR4Hbzy7pF/txRd5LrLUFxph44F1jzOfNv3W5hQuf42ia/sF/11p7ovlmlz/Hbe26jdtavybPtI1HX88dZPnyhsZMp6msJre42aOvj9NR2ritdeatNJ1ir2z+bPJ1YEgnf9bVurLP64CPrbUtj2qceI474pHXsNeUlbX28nN8iHwgucX3SUBB89dHjDEJ1trC5sPT4nPcV7t5jTFd2d81wFZr7ZEWj/3F18aYPwJveUNea21B83+LjTHLaTrMX48bnl9XZTbGhNJUVIuttctaPLbLn+M2tPea7GibsE78rKt1Ji/GmNHAn4BrrLVlp29v5/XhTh1mbvELCtbalcaYp40xfTrzs27QlX1+6YyLQ89xRzzyGvan04CbgSHGmAHNRyu3ACua71sBfLP5628CnTlSOxdd2d+Xzkk3v/medgPQ5igcF+owrzGmmzEm5vTXwJUtcnn6+e3UPo0xBvgzsNda+1ir+zzxHLf3mjxtBTC/eUTVRKC8+bRmZ37W43mNMSnAMuBWa21Wi9vbe304nfm85tcCxpjxNL3vlXXmZ53I25wzFphKi9e1g89xRzzzGvbUiJJz+UPTm0k+UAMcAd5uvj0RWNliuxk0jfjaT9Ppw9O39wbeA/Y1/7eXm/O2ub828kbR9I8mttXPvwTsAnY2/+UmOJ2XphE9O5r/7HHy+e1C5sk0nXbYCWxv/jPDk89xW69JYBGwqPlrAzzVfP8uWox2PdPr2c3Pa0d5/wQca/F8ZnT0+vCCzPc1Z9pB06CQi735OW7+/jbglVY/58hzTNMv04VAHU3vwwudeA1rBgsREfF6/nQaUERE/JTKSkREvJ7KSkREvJ7KSkREvJ7KSkREvJ7KSkREvJ7KSkREvJ7KSkREvN7/ASSwtdqMGfKGAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(-1,1,50) #等差数列\n",
    "y1 = 2*x + 1\n",
    "y2 = x**2\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(x,y1)\n",
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(x,y2)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 504x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#函数名称都挺易于理解的\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(x,y1,color='red',linewidth=1)\n",
    "plt.plot(x,y2,color='blue',linewidth=5)\n",
    "plt.xlabel('x',fontsize=20)\n",
    "plt.ylabel('y',fontsize=20)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "l1, = plt.plot(x,y1,color='red',linewidth=1)\n",
    "l2, = plt.plot(x,y2,color='blue',linewidth=5)\n",
    "plt.legend(handles=[l1,l2],labels=['aa','bb'],loc='best')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.xlim((0,1))  #x轴只截取一段进行显示\n",
    "plt.ylim((0,1))  #y轴只截取一段进行显示\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dots1 = np.array([2,3,4,5,6])\n",
    "# dots2 = np.array([2,3,4,5,6])\n",
    "dots1 =np.random.rand(50)\n",
    "dots2 =np.random.rand(50)\n",
    "plt.scatter(dots1,dots2,c='red',alpha=0.5) #c表示颜色，alpha表示透明度\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(10)\n",
    "y = 2**x+10\n",
    "plt.bar(x,y,facecolor='#9999ff',edgecolor='white')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.arange(10)\n",
    "y=2**x+10\n",
    "plt.bar(x,y,facecolor='#9999ff',edgecolor='white')\n",
    "for ax,ay in zip(x,y):\n",
    "    plt.text(ax,ay,'%.1f' % ay,ha='center',va='bottom')\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}